Skip to main content
SpringerLink
Log in

Neural Pattern Recognition with Self-organizing Maps for Efficient Processing of Forex Market Data Streams

  • Conference paper
Artificial Intelligence and Soft Computing (ICAISC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6113))

Included in the following conference series:

Abstract

The paper addresses the problem of using Japanese candlestick methodology to analyze stock or forex market data by neural nets. Self organizing maps are presented as tools for providing maps of known candlestick formations. They may be used to visualize these patterns, and as inputs for more complex trading decision systems. In that case their role is preprocessing, coding and pre-classification of price data. An example of a profitable system based on this method is presented. Simplicity and efficiency of training and network simulating algorithms is emphasized in the context of processing streams of market data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. McNelis, P.D.: Neural Networks in Finance: Gaining predictive edge in the market. Elsevier, Amsterdam (2005)

    Google Scholar 

  2. Nison, S.: Japanese Candlestick Charting Techniques, 2nd edn. Prentice Hall Press, Englewood Cliffs (2001)

    Google Scholar 

  3. Li, S.-T., Kuo, S.-C.: Knowledge discovery in financial investment for forecasting and trading strategy through wavelet-based SOM networks. Expert Systems with Applications 32(2), 935–951 (2008)

    Article  MathSciNet  Google Scholar 

  4. Vanstone, B., Finnie, G.: An empirical methodology for developing stockmarket trading systems using artificial neural networks. Expert Systems with Applications 36(3), 6668–6680 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Engineering, Control and Robotics, Wroclaw University of Technology,  

    Piotr Ciskowski & Marek Zaton

Authors
  1. Piotr Ciskowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marek Zaton
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Academy of Humanities and Economics in Łódź,, ul. Rewolucji 1905 nr 64, Łódź, Poland

    Rafał Scherer

  3. Institute of Automatics,, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Initiative in Soft Computing (BISC), 94720-1776, Berkeley, CA,  

    Lotfi A. Zadeh

  5. Computational Intelligence Laboratory Department of Electrical and Computer Engineering, University of Louisville, 40292, Louisville, KY

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ciskowski, P., Zaton, M. (2010). Neural Pattern Recognition with Self-organizing Maps for Efficient Processing of Forex Market Data Streams. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13208-7_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13208-7_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13207-0

  • Online ISBN: 978-3-642-13208-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation